A Unique Object (UNO) is a physical object with unique characteristics that can be measured externally. The usually analogue measurement can be converted into a digital representation - a fingerprint - which uniquely identifies the object. For practical applications it is necessary that measurements can be performed without the need of specialist equipment or complex measurement setup. Furthermore, a UNO should be able to defeat simulation attacks; an attacker may replace the UNO with a device or system that produces the expected measurement. Recently a novel type of UNOs based on Quantum Dots (QDs) and exhibiting unique photo-luminescence properties has been proposed. The uniqueness of these UNOs is based on quantum effects that can be interrogated using a light source and a camera. The so called Quantum Confinement UNO (QCUNO) responds uniquely to different light excitation levels which is exploited for simulation attack protection, as opposed to focusing on features too small to reproduce and therefore difficult to measure. In this paper we describe methods for extraction of fingerprints from the QCUNO. We evaluate our proposed methods using 46 UNOs in a controlled setup. Focus of the evaluation are entropy, error resilience and the ability to detect simulation attacks.

While learning algorithms have been used for astronomical data analysis, the vast majority of those algorithms have used supervised learning. In a continuation of the work described in Young et ah [18] we examine the use of unsupervised learning for this task with two types of Adaptive Resonance Theory (ART) neural networks. Using synthetic astronomical data from SkyMaker[2], [3] which was designed to mimic the dynamic range of the CTI-[14] telescope, we compared the ability of the ART-1 neural network[4] and the ART-1 neural network with category theoretic modiflcation[9], [11] to detect regions of interest and to characterize noise. We show a difference in the geometries of the templates created by each architecture. We also show an analysis of the two architectures over a range of parameter settings. The results provided show that ART neural networks and unsupervised learning algorithms in general should not be overlooked for astronomical data analysis.

Quantum dots show excellent promise as triggered sources of both single and polarization entangled photons for quantum information applications. Our recent progress developing nonclassical light sources with single quantum dots is presented in this paper. Following radiative emission of an exciton confined in a quantum dot, there is a finite delay before re-excitation can occur; this results in an anti-bunching of the photons emitted providing a source of single photons. Excitation of a quantum dot with two electrons and two holes leads to the emission of a pair of photons; we show here that, provided the spin splitting of the intermediate exciton state in the decay is erased, the photon pair is emitted in an entangled polarization state. The fidelity of this entangled state is shown to exceed 70%. Using quantum dots to generate quantum light allows contacts for electrical injection to be integrated into a compact and robust device. A cavity may also be integrated into the semiconductor structure to enhance the photon collection efficiency and control the recombination dynamics. We detail a process to form a submicrometer current aperture within an electrical device, allowing individual quantum dots to be addressed electrically in devices.

Many studies have addressed the knowledge representation capability of neural networks. A recently-developed mathematical semantic theory explains the relationship between knowledge and its representation in connectionist systems. The theory yields design principles for neural networks whose behavioral repertoire expresses any desired capability that can be expressed logically. In this paper, we show how the design principle of limit formation can he applied to modify the ART-1 architecture, yielding a discrimination capability that goes beyond vigilance. Simulations of this new design illustrate the increased discrimination ability it provides for multi-spectral image analysis.

Our analytics task is to identify, characterize, and visualize anomalous subsets of as large of a collection of network connection data as possible. We use a combination of HPC resources, advanced algorithms, and visualization techniques. To effectively and efficiently identify the salient portions of the data, we rely on a multistage workflow that includes data acquisition, summarization (feature extraction), novelty detection, and classification. Once these subsets of interest have been identified and automatically characterized, we use a stateof- the-art high-dimensional query system to extract this data for interactive visualization. Our approach is equally useful for other large-data analysis problems where it is more practical to identify interesting subsets of the data for visualization than it is to render all data elements. By reducing the size of the rendering workload, we enable highly interactive and useful visualizations.